Page 88 - Read Online

Page 474 Wang et al. Art Int Surg. 2025;5:465-75 https://dx.doi.org/10.20517/ais.2025.03

REFERENCES
1. Amputee Coalition. 5.6 million++ Americans are living with limb loss and limb difference: new study published. Amputee Coalition (
Washington, DC), February 14, 2024. Available from https://amputee-coalition.org/5-6-million-americans-living-with-limb-loss-limb-
difference/ [accessed 10 October 2025].
2. Biddiss E, Chau T. The roles of predisposing characteristics, established need, and enabling resources on upper extremity prosthesis
use and abandonment. Disabil Rehabil Assist Technol. 2007;2:71-84. DOI PubMed
3. Bosman CE, van der Sluis CK, Geertzen JHB, Kerver N, Vrieling AH. User-relevant factors influencing the prosthesis use of persons
with a transfemoral amputation or knee-disarticulation: a meta-synthesis of qualitative literature and focus group results. PLoS One.
2023;18:e0276874. DOI PubMed PMC
4. Van Der Riet D, Stopforth R, Bright G, et al. An overview and comparison of upper limb prosthetics. In: 2013 Africon; 2013 Sep 9-12;
Pointe aux Piments, Mauritius. New York: IEEE; 2014. pp. 1-8. DOI
5. Toro-Ossaba A, Tejada JC, Sanin-Villa D. Myoelectric control in rehabilitative and assistive soft exoskeletons: a comprehensive
review of trends, challenges, and integration with soft robotic devices. Biomimetics. 2025;10:214. DOI PubMed PMC
6. Santosa KB, Oliver JD, Cederna PS, et al. Regenerative Peripheral Nerve Interfaces for prevention and management of neuromas. Clin
Plast Surg. 2020; 47:311-21. DOI
7. Dumont NA, Bentzinger CF, Sincennes MC, et al. Satellite cells and skeletal muscle regeneration. Compr Physiol. 2015;5:1027-59.
DOI PubMed
8. Chávez OHG, Castillo AG, Mariscal SC, et al. Quick review and technical approach for Regenerative Peripheral Nerve Interface
surgery. MPS. 2023;13:126-31. DOI
9. Vu PP, Irwin ZT, Bullard AJ, et al. Closed-loop continuous hand control via chronic recording of Regenerative Peripheral Nerve
Interfaces. IEEE Trans Neural Syst Rehabil Eng. 2018;26:515-26. DOI PubMed
10. Irwin ZT, Schroeder KE, Vu PP, et al. Chronic recording of hand prosthesis control signals via a Regenerative Peripheral Nerve
Interface in a rhesus macaque. J Neural Eng. 2016;13:046007. DOI PubMed
11. Kung TA, Langhals NB, Martin DC, Johnson PJ, Cederna PS, Urbanchek MG. Regenerative Peripheral Nerve Interface viability and
signal transduction with an implanted electrode. Plast Reconstr Surg. 2014;133:1380-94. DOI PubMed
12. Planitzer U, Steinke H, Meixensberger J, Bechmann I, Hammer N, Winkler D. Median nerve fascicular anatomy as a basis for distal
neural prostheses. Ann Anat. 2014;196:144-9. DOI PubMed
13. Murphy KA, Morrisonponce D. Anatomy, Shoulder and Upper Limb, Median Nerve. Nih.gov. Published January 12, 2020. Available
from: https://www.ncbi.nlm.nih.gov/books/NBK448084/. [Last accessed on 12 Sep 2025].
14. Kim SP, Simeral JD, Hochberg LR, Donoghue JP, Black MJ. Neural control of computer cursor velocity by decoding motor cortical
spiking activity in humans with tetraplegia. J Neural Eng. 2008;5:455-76. DOI PubMed PMC
15. Wu W, Gao Y, Bienenstock E, Donoghue JP, Black MJ. Bayesian population decoding of motor cortical activity using a Kalman filter.
Neural Comput. 2006;18:80-118. DOI PubMed
16. Kalman RE. A new approach to linear filtering and prediction problems. J Basic Eng. 1960;82:35-45. DOI
17. Hardie R. A fast image super-resolution algorithm using an adaptive Wiener filter. IEEE Trans Image Process 2007;16:2953-64. DOI
PubMed
18. Jarrah YA, Grace AM, Samuel OW, et al. Enhancement of upper limb movement classification based on wiener filtering technique. In:
2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM); 2021 Mar 1-2; Shenzhen,
China. New York: IEEE; 2021. pp. 1-6. DOI
19. Avogaro A, Cunico F, Rosenhahn B, Setti F. Markerless human pose estimation for biomedical applications: a survey. Front Comput
Sci. 2023;5:1153160. DOI
20. Toshev A, Szegedy C. DeepPose: human pose estimation via deep neural networks. In: 2014 IEEE Conference on Computer Vision
and Pattern Recognition; 2014 Jun 23-38; Columbus, OH, USA. New York: IEEE; 2014. pp. 1653-60. DOI
21. Desmarais Y, Mottet D, Slangen P, Montesinos P. A review of 3D human pose estimation algorithms for markerless motion capture.
Comput Vis Image Und. 2021;212:103275. DOI
22. John GH, Langley P. Estimating continuous distributions in bayesian classifiers. arXiv. 2013;arXiv:13024964. Available from: https://
arxiv.org/abs/1302.4964 [accessed 12 September 2025].
23. Navone HD, Cook D, Downs T, Chen D. Boosting Naive-Bayes classifiers to predict outcomes for hip prostheses. In: IJCNN’99.
International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339); 1999 Jul 10-16; Washington, DC, USA. New
York: IEEE; 1999. pp. 3622-6. DOI
24. Yang X, Tian YL. EigenJoints-based action recognition using Naïve-Bayes-Nearest-Neighbor. In: 2012 IEEE Computer Society
Conference on Computer Vision and Pattern Recognition Workshops; 2012 Jun 16-21; Providence, RI, USA. New York: IEEE; 2012.
pp. 14-9. DOI
25. Galata A, Johnson N, Hogg D. Learning variable-length markov models of behavior. Comput Vis Image Und. 2001;81:398-413. DOI
26. Oliver NM, Rosario B, Pentland A. A Bayesian computer vision system for modeling human interactions. IEEE Trans Pattern Anal
Machine Intell. 2000;22:831-43. DOI
27. Malešević N, Marković D, Kanitz G, Controzzi M, Cipriani C, Antfolk C. Decoding of individual finger movements from surface
EMG signals using vector autoregressive hierarchical hidden Markov models (VARHHMM). In: 2017 International Conference on
Rehabilitation Robotics (ICORR); 2017 Jul 17-20; London, UK. New York: IEEE;2017. pp. 1518-23. DOI